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检索条件"任意字段=International Conference on Advances in Machine Learning and Systems Engineering"

共 69114 条记录，以下是131-140 订阅

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machine learning: The Driving Force Behind Intelligent systems and Predictive Analytics 2

Machine Learning: The Driving Force Behind Intelligent Syste...

引用

2nd IEEE international conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Virmani, Deepali Atheeq Sultan Ghori, Md. Tyagi, Nishant Ambilwade, R.P. Rajesh Patil, Priyanka Sharma, M.K. Vivekananda Institute of Professional Studies-Technical Campus School of Engineering and Technology India Telangana University Nizamabad Department of Computer Science and Engineering Telangana State India Management G.L. Bajaj Institute of Management and Research Greater Noida India Computer Science National Defence Academy Pune India Prof Ram Meghe College of Engineering and Management Department of Electronics and Telecommunication Engineering Badnera India Mathematics Chaudhary Charan Singh University Uttar Pradesh Meerut250004 India

ISBN: (纸本)9798350384277

This technical analysis explores the profound impact of machine learning on intelligent systems and predictive analytics. It examines algorithmic fundamentals and explores models such as linear regression, support vector machines and neural networks, which are the pillars of supervised learning. The research explores practical applications in natural language processing, where advanced neural network architectures such as Transformers redefine, language understanding and computer vision using convolutional neural networks for image recognition and object recognition. In addition, the analysis covers the basic steps of data processing, including data cleaning, transformation and refinement, as well as feature design and model optimization. In addition, the research addresses the challenges of machine learning, emphasizing the importance of reducing bias and ethical considerations. The study concludes with a look into the future of machine learning, envisioning advances in quantum computing to solve complex problems, combinatorial learning for distributed data problems, and synthetic data generation as a solution to data protection problems. This comprehensive study provides an overview of the current state of machine learning, challenges and promising developments in intelligent system innovation and predictive analytics. © 2024 IEEE.

关键词： Federated learning

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Development of a traffic accident prediction system using formal concept analysis and machine learning

Development of a traffic accident prediction system using fo...

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international Technical conference on Circuits/systems, Computers, and Communications (ITC-CSCC)

作者： Asai, Tomoya Nakamura, Masaki Sakakibara, Kazutoshi Motoyoshi, Tatsuo Matsumoto, Takuya Takano, Ryo Hoshikawa, Keisuke Toyama Prefectural Univ Grad Sch Engn Toyama Japan Toyama Prefectural Univ Fac Informat Engn Toyama Japan Toyama Prefectural Univ Fac Engn Toyama Japan

ISBN: (纸本)9798350379068;9798350379051

In our previous research, we analyzed past traffic accidents in Toyama Prefecture using feature engineering with formal concept analysis to support police activities and developed a method for predicting future traffic accidents using machine learning. In this study, we revise the machine learning model by using open data to predict traffic accidents at not only the locations where traffic accidents occurred in the past but also everywhere in Toyama Prefecture. Moreover, although the previous model only predicts the classification of traffic accidents, e.g., whether it is a serious injury accident or not when a traffic accident happens, we develop a machine learning model to predict whether a traffic accident tends to occur or not at a specific point.

关键词： component formatting style styling insert

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Revolutionizing Fault Prediction in MetroPT Datasets: Enhanced Diagnosis and Efficient Failure Prediction through Innovative Data Refinement 3

Revolutionizing Fault Prediction in MetroPT Datasets: Enhanc...

引用

3rd international conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Neamah, Osamah N. Bayir, Raif Graduate Institute Karabuk University Department of Mechatronics Engineering Karabuk Turkey Karabuk University Department of Mechatronics Engineering Karabuk Turkey

ISBN: (纸本)9798350349207

This scientific paper presents groundbreaking advancements in Predictive Maintenance (PdM) within Industry 4.0, employing cutting-edge machine learning classification algorithms for fault prediction and diagnosis in Air Production Unit (APU) systems like MetroPT and MetroPT-3. This research uses data-driven methodologies to optimize feature extraction techniques to enhance fault prediction and improve diagnostic accuracy. A robust and versatile model emerges through comprehensive testing, displaying exceptional potential in fault prediction and diagnosis for complex systems. The paper highlights the significance of enhanced analytical techniques, such as cross-validation, ensuring the reliability and robustness of the model, contributing to refined and accurate fault prediction and diagnosis, all without succumbing to overfitting. This work significantly advances the application of machine learning in predicting malignancy within Industry 4.0, showcasing the promise of these methodologies in fault prediction and diagnosis for intricate systems. © 2024 IEEE.

关键词： machine learning

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Human Prakruti Prediction Using machine learning Techniques 1

Human Prakruti Prediction Using Machine Learning Techniques

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1st international conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Ainapure, Bharati Pise, Reshma Bhutad, Sonali Ainapure, Ankita Murlidhar Chaudhari, Vivek Suryawanshi, Siddhesh Vishwakarma University Department of Computer Engineering Maharashtra Pune India Shah and Anchor Kutchhi Engineering College Department of Computer Engineering Maharashtra Mumbai India Hadapsar Pune India

ISBN: (纸本)9798350355611

This paper presents the results of various machine learning methods, including Multimodal Naïve base, Support Vector machine, Decision Tree, K-Nearest Neighbor,, and Random Forest in predicting human body constituencies. Ayurveda-dosha investigations have long been used;however the quantitative dependability of these diagnostic approaches remains unexplored. Careful and thorough analysis can lead to more effective treatments for predicting body constituencies. The results show that the random forest algorithm, with validation, achieves superior accurateness, recall, precision and F-score (95.2 %). The experimental findings demonstrate that the enhanced model, i.e. random forest classifier based on ensemble learning methods, significantly outperforms traditional approaches. These results suggest that advances in the proposed algorithms could lead to a promising future for Ayurveda treatments using machine learning. © 2025 IEEE.

关键词： Adversarial machine learning

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Accuracy Improvement in Diabetic Retinopathy Detection Using machine learning

Accuracy Improvement in Diabetic Retinopathy Detection Using...

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2024 IEEE international conference on Recent advances in Science and engineering Technology, ICRASET 2024

作者： Senthilselvi, A. Rajamenaga, V. Pooja, J. Ashwath, P. Abarajitha, P. Senthil Pandi, S. Srm Ist Dept of Cse Ramapuram Chennai India Srmist Dept of CSE-AIML Ramapuram Chennai India Rajalakshmi Engineering College Department of Cse Chennai India

ISBN: (纸本)9798350388602

Diabetic retinopathy, a prevalent side effect of diabetes, can cause severe vision impairment if it is not detected early. It can cause a lot of harm to life as vision happens to be a very important human *** incorporating machine learning algorithms into traditional methods of diagnosis, healthcare providers can greatly increase the prediction accuracy and efficiency of detecting diabetic retinopathy. There are many key advantages of machine learning algorithms, a primary advantage is their ability to be able to analyze a vast amount of data very quickly and highly accurately. This can then help us identify those patterns and trends that may not be immediately apparent to the human eye. In the case of diabetic retinopathy, these algorithms can analyze retinal images to detect early signs of the condition, allowing for earlier intervention and better patient outcomes. In addition, machine learning algorithms can also help reduce the subjectivity and variability that can be present in manual examinations by ophthalmologists. By providing consistent and standardized results, machine learning can improve the reliability of diagnostic assessments, which then leads to more precise and a more effective treatment plan for patients. Moreover, the integration of machine learning algorithms into healthcare systems can help address challenges such as limited resources and access to specialized care. By automating the screening process for diabetic retinopathy, healthcare providers can prioritize high-risk patients for further evaluation and treatment, this ensures that resources are being allocated efficiently and that patients receive their timely care. Overall, the research presented in this paper highlights the potential of machine learning algorithms that can revolutionize the diagnosis and management of diabetic retinopathy. By combining the strengths of technology with the expertise of healthcare professionals, it is possible to improve patient diagnosis, reduce the

关键词： Ophthalmology

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Context Aware Resource Discovery in Fog Computing: Agent Approach 2

Context Aware Resource Discovery in Fog Computing: Agent App...

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2nd IEEE international conference on advances in Information Technology, ICAIT 2024

作者： Kumbar, Basavaraj G. Bhajantri, Lokesh B. Department of Computer Science and Engineering BGMIT Mudhol VTU Karnataka Belagavi India Department of Information Science and Engineering Basaveshwar Engineering College Karnataka Bagalkote India

ISBN: (纸本)9798350383867

Fog computing offers advantages like low latency and distributed processing at the network edge. Resource discovery in heterogeneous and distributed fog nodes remain a critical research problem. While traditional approaches have been explored extensively, recent advancements in machine learning bid new prospects. This paper proposes an agent-based approach for context-aware resource discovery in fog computing that leverages machine learning. The agents autonomously discover resources while considering the context of the request and the fog networks. machine learning techniques are employed to predict resource availability and optimize resource selection. This approach aims to improve resource utilization, reduce latency, and enhance the efficiency of fog computing networks. © 2024 IEEE.

关键词： Adversarial machine learning

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Minor Damage Detection in Cars using Transfer learning Techniques 2

Minor Damage Detection in Cars using Transfer Learning Techn...

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2nd IEEE international conference on advances in Information Technology, ICAIT 2024

作者： Sungheetha, Akey Christopher, Kevin Maheswari, T. Latha Sasikalarani, K. Sharma, Rajesh R. Computer Science & Engineering Alliance University Bengaluru India Coimbatore India

ISBN: (纸本)9798350383867

The car scratch detection is a pivotal aspect of automotive maintenance and safety, crucial for preserving the visual appeal and structural integrity of vehicle exteriors. This paper investigates contemporary methodologies and advancements in car scratch detection, emphasizing both conventional and computer vision-based techniques. By scrutinizing diverse approaches including image processing algorithms, machine learning models, and sensor-driven systems, this study elucidates the efficacy and constraints of existing methodologies while delineating prospective avenues for further research and development in the realm of car scratch detection. Additionally, the paper underscores the significance of real-time detection systems and the integration of advanced technologies such as artificial intelligence and deep learning to enhance the accuracy and efficiency of car scratch detection mechanisms, ultimately contributing to the longevity and aesthetic maintenance of automotive surfaces. © 2024 IEEE.

关键词： Deep learning

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Leveraging Advanced machine learning Algorithms for Loan Repayment Prediction 15

Leveraging Advanced Machine Learning Algorithms for Loan Rep...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ranjan, Akshat Gourisaria, Mahendra Kumar Singh, Jay Prakash Panda, Amiya Ranjan Mishra, Soumya Ranjan Parihar, Varutri KIIT Deemed to be University School of Computer Engineering Odisha Bhubaneshwar India Manipal University Jaipur Dept of Computer Science & Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350370249

Accurately predicting loan repayment behavior is a critical challenge for financial institutions, which often face high default rates and financial instability due to inaccurate credit assessments. In order to address these problems, this study looks into the application of sophisticated machine learning methods such Random Forests, Decision Trees, Support Vector machines, and Stacking Classifiers. The models, trained on diverse client variables, significantly outperform traditional credit scoring methods. The findings suggest that integrating these sophisticated models can enhance financial performance, reduce default risks, and enable more informed lending decisions. The study also examines potential avenues for future research, including the application of increasingly sophisticated machine learning techniques and the integration of new data sources to enhance models. This study advances the field of machine learning in finance by providing business and academics with insightful information that could result in stronger and more secure financial systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Spam Analysis and Classification of the Dynamic Message using A Vectorizing Technique with Multi-Model machine learning Algorithm 15

Spam Analysis and Classification of the Dynamic Message usin...

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15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Gamango, Sumeet Kumar Prabavathy, A. Kethsy Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore641114 India Department of Computer Science and Engineering Karunya Institute of Technology Deemed University Karunya Nagar Tamil Nadu Coimbatore India

ISBN: (纸本)9798331300579

Spam analysis and classification of dynamic messages is an essential task in order to combat the ever-increasing volume of unsolicited and malicious emails. One effective approach is to employ a vectorizing technique along with a multi-model machine learning algorithm. This approach involves representing email messages as high-dimensional vectors, capturing various features such as word frequencies, presence of specific keywords, and structural characteristics. By transforming the text into numerical representations, the machine learning algorithm can then learn patterns and make predictions based on these representations. The use of a multi-model algorithm allows for the integration of different classification models, each with its own strengths and weaknesses, to enhance the overall performance. This approach can achieve high accuracy by leveraging diverse learning methods and combining their predictions. Furthermore, the approach is dynamic in nature, meaning that it can adapt to new forms of spam and evolving attack strategies. The key challenge lies in selecting appropriate features and tuning the parameters of the algorithm to ensure optimal performance. The results of this study can contribute to the development of more effective and efficient spam detection systems, helping users to filter out unwanted and potentially harmful messages. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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machine learning Techniques for Credit Scoring in Banking with Management, HR, and Organizational Key Components 3

Machine Learning Techniques for Credit Scoring in Banking wi...

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international conference on advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Saxena, Niti Thandayuthapani, S. Raman, P. Valavan, Thiruma A. Behera, Nihar Ranjan Khatri, Eti Jagannath Int Management Sch Management & Commerce New Delhi India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Chennai Tamil Nadu India Panimalar Engn Coll Dept Management Studies Chennai Tamil Nadu India Indian Inst Banking & Finance Mumbai Maharashtra India GBC Geneva Business Ctr Swiss Sch Business & Management SSBM Geneva Geneva Switzerland Nitte Meenakshi Inst Technol Dept Management Studies Bengaluru Karnataka India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Credit rating is crucial in the fast-changing 21st-century banking industry to determine creditworthiness. Traditional credit score systems may not be able to handle today's complex money habits because they are focused on statistics and prior data. This research advises adding management, human resources, and organizational factors to machine learning credit evaluations in addition to financial data. Structure of the research describes different machine learning types. Logistic regression, decision trees, random forests, gradient boosting, and neural networks. The algorithms are trained using this dataset's financial metrics, management practices, HR indicators, and organizational procedures. Feature engineering strategies pull data from various sources to get a full picture of someone's reputation. The research argues that machine learning models should be transparent, especially in the highly regulated banking business. Using LIME and SHAP values helps make credit scoring determinations more dependable and understandable. Credit scoring will be more precise, and financial institutions will understand credit risk aspects better. Banks can improve loan selections, portfolio performance, and risk by adding management, human resources, and organizational data to financial data. This research helps financial organizations analyze credit risk in the age of machine learning and big data, resulting in more accurate credit score models.

关键词： Credit Scoring machine learning Banking Management HR (Human Resources) Organizational Key Components Risk Assessment

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